Data Quality Lead; m​/f​/d

Role Purpose

Own the independent, end-to-end
Quality Assurance (QA) and delivery assurance
of Data Quality, Data Readiness, and Data Modelling deliverables before submission to the client.

The role will act as the
final internal quality gate
, ensuring that all deliverables are accurate, complete, consistent, traceable, and aligned with the agreed project methodology, architecture, dependencies, business requirements, and client acceptance criteria.

The ideal candidate should have strong experience in
Data Quality, Data Modelling, Data Governance, and QA
, with the ability to independently review complex deliverables and identify cross-functional inconsistencies before client submission.

• Independently QA the complete client submission package, including:

  • Data profiling outputs
  • Critical Data Element (CDE) scope
  • Data Quality rulebooks
  • Bronze and Silver execution logs
  • Failed records
  • Data Quality scores
  • Dashboards and reports
  • Annexes and supporting documentation
  • Remediation trackers
  • Closure reports and supporting evidence

• Perform detailed QA of
  data modelling deliverables
  , including conceptual, logical, and physical data models.

• Validate data model components such as:

  • Entities and attributes
  • Relationships and cardinalities
  • Primary and foreign keys
  • Naming conventions and standards
  • Data types
  • Model documentation

• Validate alignment between
  data models and downstream Data Quality deliverables
  , ensuring consistency across tables, entities, attributes, CDEs, keys, and business definitions.

• Perform
  cross-deliverable reconciliation
  to identify inconsistencies or mismatches across:

  • Scope
  • Rule IDs
  • CDEs
  • Data Quality dimensions
  • Rule counts
  • Execution results
  • Failed records
  • DQ scores
  • Timestamps
  • Remediation status
  • Dashboard reporting

• Independently reproduce or validate
  critical Data Quality calculations
  and ensure that reported Bronze and Silver scores are fully traceable to the underlying execution evidence and agreed methodology.

• Validate completeness of deliverables against agreed
  client requirements, project scope, and acceptance criteria
  , including coverage of tables, CDEs, models, DQ dimensions, rules, evidence, and required documentation.

• Review
  exclusions, exceptions, low-coverage areas, and source-system limitations
  , ensuring that appropriate rationale, impact assessment, and supporting evidence are documented.

• Maintain a structured
  QA checklist and issue register
  , ensuring all findings are properly documented and tracked through closure.

• Classify QA findings based on
  severity, business impact, and client submission risk
  .

• Work closely with Data Quality, Data Modelling, Data Governance, and other workstream teams to drive timely resolution of identified issues.

• Conduct final readiness reviews and provide a clear“Ready / Not Ready for Client Submission”recommendation.

• Strong experience in
  Data Quality Assurance, Data Governance, Data Modelling, or Data QA
  .
• Hands-on understanding of
  Conceptual, Logical, and Physical Data Models
  .
• Strong knowledge of
  data profiling, CDEs, DQ dimensions, DQ rules, scoring, and remediation processes
  .
• Experience performing
  cross-functional and cross-deliverable reconciliation
  .
• Strong SQL skills for data validation and independent verification of DQ results.
• Ability to trace reported metrics and scores back to underlying execution evidence.
• Strong understanding of
  relational databases, primary/foreign keys, relationships, cardinality, and data structures
  .
• Experience working with large and complex data transformation or data quality projects.
• Strong attention to detail with an ability to identify inconsistencies across multiple deliverables.
• Excellent analytical, problem-solving, documentation, and stakeholder management skills.
• Ability to work independently and confidently challenge deliverables before client submission.
• Experience working in consulting or client-facing environments is preferred.

• Data Quality & Data Governance
• Data Modelling
• QA & Delivery Assurance
• SQL & Data Validation
• Cross-Deliverable Reconciliation
• Root Cause Analysis
• Issue & Risk Management
• Attention to Detail
• Stakeholder Management
• Client Delivery Readiness

The successful candidate will ensure that
only complete, accurate, consistent, evidence-backed, and client-ready deliverables
are submitted, minimizing quality issues, inconsistencies, and rework during client review.